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#! pip install gradio
import gradio as gr
from transformers import pipeline
# Lade beide Modelle
vit_classifier = pipeline("image-classification", model="chrisis2/vit-food-classification-chrisis2") # Ersetze mit deinem Modell
clip_detector = pipeline(model="openai/clip-vit-large-patch14", task="zero-shot-image-classification")
# Verwende deine Food-Labels
food_labels = [
"Baked Potato", "Crispy Chicken", "Donut", "Fries", "Hot Dog", "Sandwich", "Taco", "Taquito",
"apple_pie", "burger", "butter_naan", "chai", "chapati", "cheesecake", "chicken_curry",
"chole_bhature", "dal_makhani", "dhokla", "fried_rice", "ice_cream", "idli", "jalebi",
"kaathi_rolls", "kadai_paneer", "kulfi", "masala_dosa", "momos", "omelette", "paani_puri",
"pakode", "pav_bhaji", "pizza", "samosa", "sushi"
]
def classify_food(image):
# Klassifiziere mit deinem trainierten Modell
vit_results = vit_classifier(image)
vit_output = {result['label']: result['score'] for result in vit_results}
# Klassifiziere mit CLIP Zero-Shot
clip_results = clip_detector(image, candidate_labels=food_labels)
clip_output = {result['label']: result['score'] for result in clip_results}
return {"Trainiertes ViT-Modell": vit_output, "CLIP Zero-Shot": clip_output}
# Füge Beispielbilder aus deinem Dataset hinzu
example_images = [
["gradio-food-app/example_images/burger.jpg.jpg"],
["gradio-food-app/example_images/burger.jpg.jpg"],
["gradio-food-app/example_images/burger.jpg.jpg"],
["gradio-food-app/example_images/pizza.jpg.jpg"],
["gradio-food-app/example_images/sushi.jpg.jpg"],
# Füge weitere Beispiele hinzu
]
# Erstelle die Gradio-Oberfläche
iface = gr.Interface(
fn=classify_food,
inputs=gr.Image(type="filepath"),
outputs=gr.JSON(),
title="Food Classification Vergleich",
description="Lade ein Bild eines Lebensmittels hoch und vergleiche die Ergebnisse eines trainierten ViT-Modells mit einem Zero-Shot CLIP-Modell.",
examples=example_images
)
iface.launch()
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